Studying Software Engineering Patterns for Designing Machine Learning Systems

Machine-learning (ML) techniques have become popular in the recent years. ML techniques rely on mathematics and on software engineering. Researchers and practitioners studying best practices for designing ML application systems and software to address the software complexity and quality of ML techniques. Such design practices are often formalized as architecture patterns and design patterns by encapsulating reusable solutions to commonly occurring problems within given contexts. However, to the best of our knowledge, there has been no work collecting, classifying, and discussing these software-engineering (SE) design patterns for ML techniques systematically. Thus, we set out to collect good/bad SE design patterns for ML techniques to provide developers with a comprehensive and ordered classification of such patterns. We report here preliminary results of a systematic-literature review (SLR) of good/bad design patterns for ML

Human Work Interaction Design. Designing Engaging Automation: 5th IFIP WG 13.6 Working Conference, HWID 2018, Espoo, Finland, August 20 - 21, 2018, Revised Selected Papers

International audienceBook Front Matter of AICT 54

I Trust You Dr. Researcher, but not the Company that Handles My Data –Trust in the Data Economy

In the rising era of artificial intelligence (AI), learning machinery and hyper surveillance, trust is a sought-after attribute. The General Data Protection Regulation (GDPR) was introduced to increase individuals’ control over their own personal data, yet proof of its effectiveness is still lacking. Indeed, contrary to the intentions of the GDPR recent studies have shown numerous flaws in the regulation including issues from user negligence and ignorance to manipulation via dark design patterns etc. Even informed through the compulsory privacy notices and consent, people are experiencing less trust than ever. This is impacting every area of human society. This paper reports two interview studies (N=31) that probed individuals’ trust company-driven data handling practice and communication. The results demonstrate low to no trust in the perception of data-related information given by companies, rather perceiving researchers as trustworthy in terms of correspondence between data-handling related communication and the applied reality.Papers published as part of the Proceedings of the 57th Annual Hawaii International Conference on System Sciences are under Creative Commons licenses (CC-BY-NC-ND 4.0). https://creativecommons.org/licenses/by-nc-nd/4.0/fi=vertaisarvioitu|en=peerReviewed

An experimental study for the decision-making support of shared transport options in Western Australia

The road transport systems have a direct impact on both passenger and freight movements, leading towards negative environmental effects plus economical and societal costs. It has reached an alarming level. Afford to purchase more than one vehicle by an individual who can pay for it may solve the transportation problem for them; however, not everyone has a personal vehicle for movement. Moreover, providing public transport facilities to everyone on a demand basis or on time basis is not possible for several reasons. Further, personal or commercial vehicles are not being used all the time or not carrying passengers in full though it can afford with the number of seats available. One possible solution is to share these vehicles for minimising the number of vehicles and to ensure the effective use of vehicles. Thus, the shared transport systems, such as ride-sharing, car-sharing, car-pooling, or bike-sharing are the promising choice to facilitate good accessibilities to the city or urban living population, especially in a densely populated area. In this paper, we have worked with such shared transport options by collecting data through an online survey and analysed data in various ways. We have also designed and developed a decision support system for this data to offer the city planners in Western Australia to make informed decisions about the future transportation systems for their people. Finally, we have predicted on the use of rail transport, for example, how a train station can be used to facilitate the shared transport options for better accessibility